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Multitasking of Microsoft Windows 1.01 released in 1985, here shown running the MS-DOS Executive and Calculator programs. In computing, multitasking is the concurrent execution of multiple tasks (also known as processes) over a certain period of time. New tasks can interrupt already started ones before they finish, instead of waiting for them ...
Even though it is very difficult to further speed up a single thread or single program, most computer systems are actually multitasking among multiple threads or programs. Thus, techniques that improve the throughput of all tasks result in overall performance gains. Two major techniques for throughput computing are multithreading and ...
Multitasking may refer to: Computer multitasking, the concurrent execution of multiple tasks (also known as processes) over a certain period of time Cooperative multitasking; Pre-emptive multitasking; Human multitasking, the apparent performance by an individual of handling more than one task at the same time
Cooperative multitasking is similar to async/await in languages, such as JavaScript or Python, that feature a single-threaded event-loop in their runtime. This contrasts with cooperative multitasking in that await cannot be invoked from a non-async function, but only an async function, which is a kind of coroutine. [4] [5]
Explicitly parallel instruction computing (EPIC) is like VLIW with extra cache prefetching instructions. Simultaneous multithreading (SMT) is a technique for improving the overall efficiency of superscalar processors. SMT permits multiple independent threads of execution to better utilize the resources provided by modern processor architectures.
A cc–NUMA system is a cluster of SMP systems – each called a "node", which can have a single processor, a multi-core processor, or a mix of the two, of one or other kinds of architecture – connected via a high-speed "connection network" that can be a "link" that can be a single or double-reverse ring, or multi-ring, point-to-point ...
In processor design, there are two ways to increase on-chip parallelism with fewer resource requirements: one is superscalar technique which tries to exploit instruction-level parallelism (ILP); the other is multithreading approach exploiting thread-level parallelism (TLP).
Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks.